




'''simple docstring'''


import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402

class      A       (						unittest.TestCase       ):







							def   __lowerCAmelCase     (    self			:    Optional[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_a      	=							0







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


														_a      	=							AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32'''   )
														self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Optional[int]:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							Path(lowerCAmelCase_   ) / '''preprocessor_config.json'''
																					_a      	=							Path(lowerCAmelCase_   ) / '''config.json'''
																					json.dump(
																					    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}				,     open(lowerCAmelCase_				,     '''w'''   )				,     )
																					json.dump({'''model_type''': '''clip'''}				,     open(lowerCAmelCase_				,     '''w'''   )   )

																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Any:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							Path(lowerCAmelCase_   ) / '''preprocessor_config.json'''
																					_a      	=							Path(lowerCAmelCase_   ) / '''config.json'''
																					json.dump(
																					    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}				,     open(lowerCAmelCase_				,     '''w'''   )				,     )
																					json.dump({'''model_type''': '''clip'''}				,     open(lowerCAmelCase_				,     '''w'''   )   )

																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Any   )     ->				List[str]:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							CLIPConfig()

																					# Create a dummy config file with image_proceesor_type
																					_a      	=							Path(lowerCAmelCase_   ) / '''preprocessor_config.json'''
																					_a      	=							Path(lowerCAmelCase_   ) / '''config.json'''
																					json.dump(
																					    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}				,     open(lowerCAmelCase_				,     '''w'''   )				,     )
																					json.dump({'''model_type''': '''clip'''}				,     open(lowerCAmelCase_				,     '''w'''   )   )

																					# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_   ).to_dict()

																					config_dict.pop('''image_processor_type'''   )
																					_a      	=							CLIPImageProcessor(**lowerCAmelCase_   )

																					# save in new folder
																					model_config.save_pretrained(lowerCAmelCase_   )
																					config.save_pretrained(lowerCAmelCase_   )

																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_   )

																					# make sure private variable is not incorrectly saved
																					_a      	=							json.loads(config.to_json_string()   )
																					self.assertTrue('''_processor_class''' not in dict_as_saved   )

														self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Tuple   )     ->				Dict:


														"""simple docstring"""


														with tempfile.TemporaryDirectory() as tmpdirname:
																					_a      	=							Path(lowerCAmelCase_   ) / '''preprocessor_config.json'''
																					json.dump(
																					    {'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''}				,     open(lowerCAmelCase_				,     '''w'''   )				,     )

																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_   )
																					self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				str:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_				,     '''clip-base is not a local folder and is not a valid model identifier'''   ):
																					_a      	=							AutoImageProcessor.from_pretrained('''clip-base'''   )







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				int:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_				,     R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''   ):
																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_				,     revision='''aaaaaa'''   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Any:


														"""simple docstring"""


														with self.assertRaisesRegex(
														    lowerCAmelCase_				,     '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.'''				,     ):
																					_a      	=							AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model'''   )







							def   __lowerCAmelCase     (    self			:    Union[str, Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														with self.assertRaises(lowerCAmelCase_   ):
																					_a      	=							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''   )
														# If remote code is disabled, we can't load this config.
														with self.assertRaises(lowerCAmelCase_   ):
																					_a      	=							AutoImageProcessor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_image_processor'''				,     trust_remote_code=lowerCAmelCase_   )

														_a      	=							AutoImageProcessor.from_pretrained(
														    '''hf-internal-testing/test_dynamic_image_processor'''				,     trust_remote_code=lowerCAmelCase_   )
														self.assertEqual(image_processor.__class__.__name__				,     '''NewImageProcessor'''   )

														# Test image processor can be reloaded.
														with tempfile.TemporaryDirectory() as tmp_dir:
																					image_processor.save_pretrained(lowerCAmelCase_   )
																					_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_				,     trust_remote_code=lowerCAmelCase_   )
														self.assertEqual(reloaded_image_processor.__class__.__name__				,     '''NewImageProcessor'''   )







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Any:


														"""simple docstring"""


														try:
																					AutoConfig.register('''custom'''				,     lowerCAmelCase_   )
																					AutoImageProcessor.register(lowerCAmelCase_				,     lowerCAmelCase_   )
																					# Trying to register something existing in the Transformers library will raise an error
																					with self.assertRaises(lowerCAmelCase_   ):
																												AutoImageProcessor.register(lowerCAmelCase_				,     lowerCAmelCase_   )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																												_a      	=							Path(lowerCAmelCase_   ) / '''preprocessor_config.json'''
																												_a      	=							Path(lowerCAmelCase_   ) / '''config.json'''
																												json.dump(
																												    {'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''}				,     open(lowerCAmelCase_				,     '''w'''   )				,     )
																												json.dump({'''model_type''': '''clip'''}				,     open(lowerCAmelCase_				,     '''w'''   )   )

																												_a      	=							CustomImageProcessor.from_pretrained(lowerCAmelCase_   )

																					# Now that the config is registered, it can be used as any other config with the auto-API
																					with tempfile.TemporaryDirectory() as tmp_dir:
																												image_processor.save_pretrained(lowerCAmelCase_   )
																												_a      	=							AutoImageProcessor.from_pretrained(lowerCAmelCase_   )
																												self.assertIsInstance(lowerCAmelCase_				,     lowerCAmelCase_   )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																												del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Any:


														"""simple docstring"""


														class      A       (						_a       ):
																					lowercase_							     =							True

														try:
																					AutoConfig.register('''custom'''				,     lowerCAmelCase_   )
																					AutoImageProcessor.register(lowerCAmelCase_				,     lowerCAmelCase_   )
																					# If remote code is not set, the default is to use local
																					_a      	=							AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor'''   )
																					self.assertEqual(image_processor.__class__.__name__				,     '''NewImageProcessor'''   )
																					self.assertTrue(image_processor.is_local   )

																					# If remote code is disabled, we load the local one.
																					_a      	=							AutoImageProcessor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_image_processor'''				,     trust_remote_code=lowerCAmelCase_   )
																					self.assertEqual(image_processor.__class__.__name__				,     '''NewImageProcessor'''   )
																					self.assertTrue(image_processor.is_local   )

																					# If remote is enabled, we load from the Hub
																					_a      	=							AutoImageProcessor.from_pretrained(
																					    '''hf-internal-testing/test_dynamic_image_processor'''				,     trust_remote_code=lowerCAmelCase_   )
																					self.assertEqual(image_processor.__class__.__name__				,     '''NewImageProcessor'''   )
																					self.assertTrue(not hasattr(lowerCAmelCase_				,     '''is_local'''   )   )

														finally:
																					if "custom" in CONFIG_MAPPING._extra_content:
																												del CONFIG_MAPPING._extra_content["custom"]
																					if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																												del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







"""simple docstring"""




from __future__ import annotations

from decimal import Decimal

from numpy import array



def        a__					(							snake_case__							)		->   list[list[float]]:
			lowerCamelCase      =   Decimal

			# Check if the provided matrix has 2 rows and 2 columns
			# since this implementation only works for 2x2 matrices
			if len(snake_case__							) == 2 and len(matrix[0]							) == 2 and len(matrix[1]							) == 2:
						# Calculate the determinant of the matrix
						lowerCamelCase      =   float(
						    d(matrix[0][0]							) * d(matrix[1][1]							) - d(matrix[1][0]							) * d(matrix[0][1]							)							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creates a copy of the matrix with swapped positions of the elements
						lowerCamelCase      =   [[0.0, 0.0], [0.0, 0.0]]
						lowerCamelCase ,  lowerCamelCase      =   matrix[1][1], matrix[0][0]
						lowerCamelCase ,  lowerCamelCase      =   -matrix[1][0], -matrix[0][1]

						# Calculate the inverse of the matrix
						return [
						    [(float(d(snake_case__							)							) / determinant) or 0.0 for n in row] for row in swapped_matrix
						]
			elif (
			    len(snake_case__							) == 3
			    and len(matrix[0]							) == 3
			    and len(matrix[1]							) == 3
			    and len(matrix[2]							) == 3
			):
						# Calculate the determinant of the matrix using Sarrus rule
						lowerCamelCase      =   float(
						    (
						        (d(matrix[0][0]							) * d(matrix[1][1]							) * d(matrix[2][2]							))
						        + (d(matrix[0][1]							) * d(matrix[1][2]							) * d(matrix[2][0]							))
						        + (d(matrix[0][2]							) * d(matrix[1][0]							) * d(matrix[2][1]							))
						    )
						    - (
						        (d(matrix[0][2]							) * d(matrix[1][1]							) * d(matrix[2][0]							))
						        + (d(matrix[0][1]							) * d(matrix[1][0]							) * d(matrix[2][2]							))
						        + (d(matrix[0][0]							) * d(matrix[1][2]							) * d(matrix[2][1]							))
						    )							)
						if determinant == 0:
									raise ValueError("""This matrix has no inverse."""							)

						# Creating cofactor matrix
						lowerCamelCase      =   [
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						    [d(0.0							), d(0.0							), d(0.0							)],
						]
						lowerCamelCase      =   (d(matrix[1][1]							) * d(matrix[2][2]							)) - (
						    d(matrix[1][2]							) * d(matrix[2][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[1][0]							) * d(matrix[2][2]							)) - (d(matrix[1][2]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[1][0]							) * d(matrix[2][1]							)) - (
						    d(matrix[1][1]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][1]							) * d(matrix[2][2]							)) - (d(matrix[0][2]							) * d(matrix[2][1]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[2][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[2][0]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[2][1]							)) - (d(matrix[0][1]							) * d(matrix[2][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][1]							) * d(matrix[1][2]							)) - (
						    d(matrix[0][2]							) * d(matrix[1][1]							)
						)
						lowerCamelCase      =   -(
						    (d(matrix[0][0]							) * d(matrix[1][2]							)) - (d(matrix[0][2]							) * d(matrix[1][0]							))
						)
						lowerCamelCase      =   (d(matrix[0][0]							) * d(matrix[1][1]							)) - (
						    d(matrix[0][1]							) * d(matrix[1][0]							)
						)

						# Transpose the cofactor matrix (Adjoint matrix)
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												lowerCamelCase      =   cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
						lowerCamelCase      =   array(snake_case__							)
						for i in range(3							):
									for j in range(3							):
												inverse_matrix[i][j] /= d(snake_case__							)

        # Calculate the inverse of the matrix
						return [[float(d(snake_case__							)							) or 0.0 for n in row] for row in inverse_matrix]
			raise ValueError("""Please provide a matrix of size 2x2 or 3x3."""							)



